Abstract: Embodiments of the present application disclose a deep learning-based face identification method and a related product. The method is applied to an electronic apparatus. The method includes: acquiring an aligned face image, and scaling the face image at a preset ratio to obtain a target image; extracting a pixel matrix of the target image, inputting the pixel matrix to the neural network model, and executing multilayer computing to obtain a computing result; and calculating a cosine similarity degree between the computing result and a facial template vector, wherein comparison is successful if the cosine similarity degree is greater than a preset threshold. The embodiments of the present application help improve the identification success rate and the identification speed.

Abstract: Embodiments of the present application disclose a deep learning-based face identification method and a related product. The method is applied to an electronic apparatus. The method includes: acquiring an aligned face image, and scaling the face image at a preset ratio to obtain a target image; extracting a pixel matrix of the target image, inputting the pixel matrix to the neural network model, and executing multilayer computing to obtain a computing result; and calculating a cosine similarity degree between the computing result and a facial template vector, wherein comparison is successful if the cosine similarity degree is greater than a preset threshold. The embodiments of the present application help improve the identification success rate and the identification speed.